direct_lidar_odometry/include/nano_gicp/nanoflann.hpp

/************************************************************
 *
 * Copyright (c) 2021, University of California, Los Angeles
 *
 * Authors: Kenny J. Chen, Brett T. Lopez
 * Contact: kennyjchen@ucla.edu, btlopez@ucla.edu
 *
 ***********************************************************/

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 * Software License Agreement (BSD License)
 *
 * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
 * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
 * Copyright 2011-2021  Jose Luis Blanco (joseluisblancoc@gmail.com).
 *   All rights reserved.
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#ifndef NANO_KDTREE_KDTREE_FLANN_H_
#define NANO_KDTREE_KDTREE_FLANN_H_

#include <boost/shared_ptr.hpp>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/kdtree/kdtree_flann.h>
#include "nano_gicp/impl/nanoflann_impl.hpp"

namespace nanoflann
{

template <typename PointT>
class KdTreeFLANN
{
public:

  typedef typename pcl::PointCloud<PointT> PointCloud;
  typedef typename pcl::PointCloud<PointT>::Ptr PointCloudPtr;
  typedef typename pcl::PointCloud<PointT>::ConstPtr PointCloudConstPtr;

  typedef boost::shared_ptr<KdTreeFLANN<PointT>> Ptr;
  typedef boost::shared_ptr<const KdTreeFLANN<PointT>> ConstPtr;
  typedef boost::shared_ptr<std::vector<int>> IndicesPtr;
  typedef boost::shared_ptr<const std::vector<int>> IndicesConstPtr;

  KdTreeFLANN (bool sorted = false);
  KdTreeFLANN (const KdTreeFLANN<PointT> &k);

  void  setEpsilon (float eps);

  void  setSortedResults (bool sorted);

  inline Ptr makeShared () { return Ptr (new KdTreeFLANN<PointT> (*this)); }

  void setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices = IndicesConstPtr ());

  inline PointCloudConstPtr getInputCloud() const { return _adaptor.pcl; }

  int  nearestKSearch (const PointT &point, int k, std::vector<int> &k_indices,
                       std::vector<float> &k_sqr_distances) const;

  int radiusSearch (const PointT &point, double radius, std::vector<int> &k_indices,
                    std::vector<float> &k_sqr_distances) const;

protected:

  nanoflann::SearchParams _params;

  struct PointCloud_Adaptor
  {
    inline size_t kdtree_get_point_count() const;
    inline float kdtree_get_pt(const size_t idx, int dim) const;
    template <class BBOX> bool kdtree_get_bbox(BBOX&) const { return false; }
    PointCloudConstPtr pcl;
    IndicesConstPtr indices;
  };

  typedef nanoflann::KDTreeSingleIndexAdaptor<
    nanoflann::SO3_Adaptor<float, PointCloud_Adaptor > ,
    PointCloud_Adaptor, 3, int> KDTreeFlann_PCL_SO3;

  PointCloud_Adaptor _adaptor;

  KDTreeFlann_PCL_SO3 _kdtree;

};

//---------- Definitions ---------------------

template<typename PointT> inline
KdTreeFLANN<PointT>::KdTreeFLANN(bool sorted):
  _kdtree(3,_adaptor, KDTreeSingleIndexAdaptorParams(100))
{
  _params.sorted = sorted;
}

template<typename PointT> inline
void KdTreeFLANN<PointT>::setEpsilon(float eps)
{
  _params.eps = eps;
}

template<typename PointT> inline
void KdTreeFLANN<PointT>::setSortedResults(bool sorted)
{
  _params.sorted = sorted;
}

template<typename PointT> inline
void KdTreeFLANN<PointT>::setInputCloud(const KdTreeFLANN::PointCloudConstPtr &cloud,
                                        const IndicesConstPtr &indices)
{
  _adaptor.pcl = cloud;
  _adaptor.indices = indices;
  _kdtree.buildIndex();
}

template<typename PointT> inline
int KdTreeFLANN<PointT>::nearestKSearch(const PointT &point, int num_closest,
                                std::vector<int> &k_indices,
                                std::vector<float> &k_sqr_distances) const
{
  k_indices.resize(num_closest);
  k_sqr_distances.resize(num_closest);

  nanoflann::KNNResultSet<float,int> resultSet(num_closest);
  resultSet.init( k_indices.data(), k_sqr_distances.data());
  _kdtree.findNeighbors(resultSet, point.data, nanoflann::SearchParams() );
  return resultSet.size();
}

template<typename PointT> inline
int KdTreeFLANN<PointT>::radiusSearch(const PointT &point, double radius,
                              std::vector<int> &k_indices,
                              std::vector<float> &k_sqr_distances) const
{
  static std::vector<std::pair<int, float> > indices_dist;
  indices_dist.reserve( 128 );

  RadiusResultSet<float, int> resultSet(radius, indices_dist);
  const size_t nFound = _kdtree.findNeighbors(resultSet, point.data, _params);

  if (_params.sorted)
    std::sort(indices_dist.begin(), indices_dist.end(), IndexDist_Sorter() );

  k_indices.resize(nFound);
  k_sqr_distances.resize(nFound);
  for(int i=0; i<nFound; i++ ){
    k_indices[i]       = indices_dist[i].first;
    k_sqr_distances[i] = indices_dist[i].second;
  }
  return nFound;
}

template<typename PointT> inline
size_t KdTreeFLANN<PointT>::PointCloud_Adaptor::kdtree_get_point_count() const {
  if( indices ) return indices->size();
  if( pcl)  return pcl->points.size();
  return 0;
}

template<typename PointT> inline
float KdTreeFLANN<PointT>::PointCloud_Adaptor::kdtree_get_pt(const size_t idx, int dim) const{
  const PointT& p = ( indices ) ? pcl->points[(*indices)[idx]] : pcl->points[idx];
  if (dim==0) return p.x;
  else if (dim==1) return p.y;
  else if (dim==2) return p.z;
  else return 0.0;
}

}


#endif